Detective AI Software Solutions

Challenges

Challenges businesses face in detecting and monitoring live activities.

Difficulty in Staying Ahead of Evolving Threats

Financial losses and reputational damage due to theft incidents and unable to track and gather the evidence due to unavailability of systems.

Without the automation the manual processes are time consuming which can be prone to human error and inconsistencies.

Large volumes of data are difficult for human investigators to uncover, due to which industries miss important clues or patterns.

Without the real-time monitoring and alert capabilities of industries struggle to respond quickly to emerging incidents or threats, leading to increased damage or disruption.

High increase in the exposure to cyberattacks, data breaches, and other digital threats which should remain confidential to the company.

Solutions

Solutions to overcome the challenges faced by businesses.

Implementation of Wize Observer

Wize Observer can analyze patterns, user behaviors, and gestures to identify theft, quickly detect the person, and send alerts.

Advance AI and Deep Learning

The adoption of AI can automate various investigations, data collection, report generation, and analytical tasks, streamlining processes and reducing the risk of human error.

Advance Data Analytics

Leveraging AI capabilities, Wet can quickly process large volumes of structured and unstructured data, identify relevant insights, and uncover hidden patterns that human investigators may miss.

Optimizing Processing Frameworks

Without the real-time monitoring and alert capabilities, industries struggle to respond quickly to emerging incidents or threats, leading to increased damage or disruption.

Anomaly Detection

Integrating detective AI with robust data security measures, such as encryption and access controls, to protect sensitive information and mitigate the risk of cyberattacks or data breaches.

Detective AI Development Services

Providing professional detective AI development services that businesses need.

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Frequently Asked Questions

Detective AI utilizes advanced AI and deep learning techniques, including Convolutional Neural Networks (CNNs) implemented with Pytorch and Yolov8, to train models that recognize diverse shoplifting scenarios with high accuracy. We also implement sophisticated object detection algorithms from OpenCV and StrongSort to refine detection capabilities and significantly reduce false positives. Our continuous model training with high-quality annotated data ensures that the AI remains precise and reliable in detecting genuine incidents while minimizing distractions and inefficiencies caused by false alarms.

To ensure the quality and effectiveness of our AI models, we use image processing and data augmentation techniques via OpenCV to enhance the quality of CCTV footage. This process allows us to overcome issues related to low-quality video feeds. Additionally, we apply various data augmentation techniques to increase the diversity of our training data, which enhances the robustness and accuracy of our models. By continuously updating and refining our datasets, we maintain high standards in model training and performance.

Yes, the Detective AI system is designed to handle real-time processing of CCTV feeds with low latency and high throughput. We leverage GPUs and optimized deep learning models to ensure efficient real-time processing. Our scalable and modular approach, utilizing open-source frameworks like Pytorch and OpenCV, helps reduce licensing costs and simplifies integration by requiring only RTSP links of CCTV feeds. This setup ensures that our system can efficiently manage continuous data streams without bottlenecks, providing seamless and prompt surveillance capabilities.